Datasets:
pwc-archive
/
links-between-paper-and-code

Dataset card Data Studio Files
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Community
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https://paperswithcode.com/paper/finite-step-performance-of-first-order
Finite Step Performance of First-order Methods Using Interpolation Conditions Without Function Evaluations
2005.01825
http://arxiv.org/abs/2005.01825v2
http://arxiv.org/pdf/2005.01825v2.pdf
https://github.com/BruceDLee/nonasymptoticOptimizationConvergence
true
true
false
none
https://paperswithcode.com/paper/a-simple-and-broadly-applicable-definition-of
A simple and broadly-applicable definition of shear transformation zones
2007.08181
http://arxiv.org/abs/2007.08181v1
http://arxiv.org/pdf/2007.08181v1.pdf
https://gitlab.com/davricha/softspot
true
true
false
none
https://paperswithcode.com/paper/cellwise-robust-m-regression
Cellwise Robust M Regression
1912.03407
http://arxiv.org/abs/1912.03407v2
http://arxiv.org/pdf/1912.03407v2.pdf
https://github.com/SebastiaanHoppner/CRM
true
true
false
none
https://paperswithcode.com/paper/fast-independent-vector-extraction-by
Fast Independent Vector Extraction by Iterative SINR Maximization
1910.10654
http://arxiv.org/abs/1910.10654v1
http://arxiv.org/pdf/1910.10654v1.pdf
https://github.com/onolab-tmu/code_2020ICASSP_five
true
true
true
none
https://paperswithcode.com/paper/a-general-optimization-based-framework-for-1
A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors
1901.03642
http://arxiv.org/abs/1901.03642v1
http://arxiv.org/pdf/1901.03642v1.pdf
https://github.com/pjrambo/VINS-Fusion-gpu
false
false
true
none
https://paperswithcode.com/paper/a-general-optimization-based-framework-for
A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors
1901.03638
http://arxiv.org/abs/1901.03638v1
http://arxiv.org/pdf/1901.03638v1.pdf
https://github.com/pjrambo/VINS-Fusion-gpu
false
false
true
none
https://paperswithcode.com/paper/srperf-a-performance-evaluation-framework-for
SRPerf: a Performance Evaluation Framework for IPv6 Segment Routing
2001.06182
http://arxiv.org/abs/2001.06182v2
http://arxiv.org/pdf/2001.06182v2.pdf
https://github.com/SRouting/SRPerf
true
true
true
none
https://paperswithcode.com/paper/nima-neural-image-assessment
NIMA: Neural Image Assessment
1709.05424
http://arxiv.org/abs/1709.05424v2
http://arxiv.org/pdf/1709.05424v2.pdf
https://github.com/Mind23-2/MindCode-101/tree/main/nima
false
false
false
mindspore
https://paperswithcode.com/paper/beyond-metadata-code-centric-and-usage-based
Beyond Metadata: Code-centric and Usage-based Analysis of Known Vulnerabilities in Open-source Software
1806.05893
http://arxiv.org/abs/1806.05893v3
http://arxiv.org/pdf/1806.05893v3.pdf
https://github.com/NAIST-SE/steady
false
false
true
none
https://paperswithcode.com/paper/code-based-vulnerability-detection-in-node-js
Code-based Vulnerability Detection in Node.js Applications: How far are we?
2008.04568
http://arxiv.org/abs/2008.04568v1
http://arxiv.org/pdf/2008.04568v1.pdf
https://github.com/NAIST-SE/steady
true
true
true
none
https://paperswithcode.com/paper/quadratization-in-discrete-optimization-and
Quadratization in discrete optimization and quantum mechanics
1901.04405
https://arxiv.org/abs/1901.04405v2
https://arxiv.org/pdf/1901.04405v2.pdf
https://github.com/cchang5/quantum_poly_solver
false
false
true
none
https://paperswithcode.com/paper/clover-convnet-line-fitting-of-velocities-in
CLOVER: Convnet Line-fitting Of Velocities in Emission-line Regions
1909.08727
http://arxiv.org/abs/1909.08727v1
http://arxiv.org/pdf/1909.08727v1.pdf
https://github.com/jakeown/astroclover
true
true
true
tf
https://paperswithcode.com/paper/generation-of-gelsight-tactile-images-for
Generation of GelSight Tactile Images for Sim2Real Learning
2101.07169
https://arxiv.org/abs/2101.07169v1
https://arxiv.org/pdf/2101.07169v1.pdf
https://github.com/danfergo/gelsight_simulation
true
false
true
none
https://paperswithcode.com/paper/dm2gal-mapping-dark-matter-to-galaxies-with
dm2gal: Mapping Dark Matter to Galaxies with Neural Networks
2012.00186
https://arxiv.org/abs/2012.00186v1
https://arxiv.org/pdf/2012.00186v1.pdf
https://github.com/nkasmanoff/dm2gal
true
true
false
pytorch
https://paperswithcode.com/paper/systolic-cnn-an-opencl-defined-scalable-run
Systolic-CNN: An OpenCL-defined Scalable Run-time-flexible FPGA Accelerator Architecture for Accelerating Convolutional Neural Network Inference in Cloud/Edge Computing
2012.03177
https://arxiv.org/abs/2012.03177v1
https://arxiv.org/pdf/2012.03177v1.pdf
https://github.com/PSCLab-ASU/SystolicCNN
true
true
false
none
https://paperswithcode.com/paper/fltrust-byzantine-robust-federated-learning
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping
2012.13995
https://arxiv.org/abs/2012.13995v3
https://arxiv.org/pdf/2012.13995v3.pdf
https://github.com/JiyaSu/ourFLTrust
false
false
false
tf
https://paperswithcode.com/paper/quantum-simulations-of-molecular-systems-with
Quantum simulations of molecular systems with intrinsic atomic orbitals
2011.08137
https://arxiv.org/abs/2011.08137v3
https://arxiv.org/pdf/2011.08137v3.pdf
https://github.com/StefanoBarison/quantum_simulation_with_IAO
true
true
true
none
https://paperswithcode.com/paper/beyond-occam-s-razor-in-system-identification
Beyond Occam's Razor in System Identification: Double-Descent when Modeling Dynamics
2012.06341
https://arxiv.org/abs/2012.06341v2
https://arxiv.org/pdf/2012.06341v2.pdf
https://github.com/antonior92/narx-double-descent
true
true
true
pytorch
https://paperswithcode.com/paper/development-and-evaluation-of-a-deep-learning
Development and evaluation of a deep learning model for protein-ligand binding affinity prediction
1712.07042
http://arxiv.org/abs/1712.07042v2
http://arxiv.org/pdf/1712.07042v2.pdf
https://github.com/code-implementation1/Code9/tree/main/pafnucy
false
false
false
mindspore
https://paperswithcode.com/paper/deep-n-ary-error-correcting-output-codes
Deep N-ary Error Correcting Output Codes
2009.10465
https://arxiv.org/abs/2009.10465v4
https://arxiv.org/pdf/2009.10465v4.pdf
https://github.com/IsaacChanghau/DeepNaryECOC
true
false
true
tf
https://paperswithcode.com/paper/context-matters-graph-based-self-supervised
Context Matters: Graph-based Self-supervised Representation Learning for Medical Images
2012.06457
https://arxiv.org/abs/2012.06457v1
https://arxiv.org/pdf/2012.06457v1.pdf
https://github.com/batmanlab/Context_Aware_SSL
true
true
true
pytorch
https://paperswithcode.com/paper/bottom-up-and-top-down-attention-for-image
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
1707.07998
http://arxiv.org/abs/1707.07998v3
http://arxiv.org/pdf/1707.07998v3.pdf
https://github.com/ZephyrZhuQi/ssbaseline
false
false
true
pytorch
https://paperswithcode.com/paper/bilinear-attention-networks
Bilinear Attention Networks
1805.07932
http://arxiv.org/abs/1805.07932v2
http://arxiv.org/pdf/1805.07932v2.pdf
https://github.com/ZephyrZhuQi/ssbaseline
false
false
true
pytorch
https://paperswithcode.com/paper/towards-vqa-models-that-can-read
Towards VQA Models That Can Read
1904.08920
https://arxiv.org/abs/1904.08920v2
https://arxiv.org/pdf/1904.08920v2.pdf
https://github.com/ZephyrZhuQi/ssbaseline
false
false
true
pytorch
https://paperswithcode.com/paper/pythia-v01-the-winning-entry-to-the-vqa
Pythia v0.1: the Winning Entry to the VQA Challenge 2018
1807.09956
http://arxiv.org/abs/1807.09956v2
http://arxiv.org/pdf/1807.09956v2.pdf
https://github.com/ZephyrZhuQi/ssbaseline
false
false
true
pytorch
https://paperswithcode.com/paper/ctrlsum-towards-generic-controllable-text-1
CTRLsum: Towards Generic Controllable Text Summarization
2012.04281
https://arxiv.org/abs/2012.04281v1
https://arxiv.org/pdf/2012.04281v1.pdf
https://github.com/salesforce/ctrl-sum
true
true
true
pytorch
https://paperswithcode.com/paper/efficient-incorporation-of-multiple-latency
Efficient Incorporation of Multiple Latency Targets in the Once-For-All Network
2012.06748
https://arxiv.org/abs/2012.06748v1
https://arxiv.org/pdf/2012.06748v1.pdf
https://github.com/vidhur2k/CS8803-Project
true
true
false
pytorch
https://paperswithcode.com/paper/generative-myocardial-motion-tracking-via
Generative Myocardial Motion Tracking via Latent Space Exploration with Biomechanics-informed Prior
2206.03830
https://arxiv.org/abs/2206.03830v1
https://arxiv.org/pdf/2206.03830v1.pdf
https://github.com/cq615/bigm-motion-tracking
true
true
false
pytorch
https://paperswithcode.com/paper/unsupervised-opinion-summarization-with-1
Unsupervised Opinion Summarization with Content Planning
2012.07808
https://arxiv.org/abs/2012.07808v1
https://arxiv.org/pdf/2012.07808v1.pdf
https://github.com/rktamplayo/PlanSum
true
true
false
pytorch
https://paperswithcode.com/paper/the-empathic-framework-for-task-learning-from
The EMPATHIC Framework for Task Learning from Implicit Human Feedback
2009.13649
https://arxiv.org/abs/2009.13649v3
https://arxiv.org/pdf/2009.13649v3.pdf
https://github.com/Pearl-UTexas/EMPATHIC
false
false
true
pytorch
https://paperswithcode.com/paper/clusterslice-a-zero-touch-deployment-platform
ClusterSlice: A Zero-touch Deployment Platform for the Edge Cloud Continuum
2403.10954
https://arxiv.org/abs/2403.10954v1
https://arxiv.org/pdf/2403.10954v1.pdf
https://github.com/swnrg/clusterslice
true
true
false
none
https://paperswithcode.com/paper/testing-the-simplifying-assumption-in-high
Testing the simplifying assumption in high-dimensional vine copulas
1706.02338
https://arxiv.org/abs/1706.02338v4
https://arxiv.org/pdf/1706.02338v4.pdf
https://github.com/MalteKurz/pacotest
true
false
false
none
https://paperswithcode.com/paper/group-communication-with-context-codec-for
Group Communication with Context Codec for Lightweight Source Separation
2012.07291
https://arxiv.org/abs/2012.07291v2
https://arxiv.org/pdf/2012.07291v2.pdf
https://github.com/yluo42/GC3
true
true
true
pytorch
https://paperswithcode.com/paper/dual-path-transformer-network-direct-context-1
Dual-Path Transformer Network: Direct Context-Aware Modeling for End-to-End Monaural Speech Separation
2007.13975
https://arxiv.org/abs/2007.13975v3
https://arxiv.org/pdf/2007.13975v3.pdf
https://github.com/yluo42/GC3
false
false
true
pytorch
https://paperswithcode.com/paper/multifidelity-multilevel-monte-carlo-to
Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes
2110.14082
https://arxiv.org/abs/2110.14082v2
https://arxiv.org/pdf/2110.14082v2.pdf
https://github.com/davidwarne/mlmcandmultifidelityforabc
true
true
false
none
https://paperswithcode.com/paper/bomuda-boundless-multi-source-domain-adaptive
BoMuDANet: Unsupervised Adaptation for Visual Scene Understanding in Unstructured Driving Environments
2010.03523
https://arxiv.org/abs/2010.03523v3
https://arxiv.org/pdf/2010.03523v3.pdf
https://github.com/divyakraman/BoMuDA-Boundless-Multi-Source-Domain-Adaptive-Segmentation-in-Unstructured-Environments
false
false
true
pytorch
https://paperswithcode.com/paper/efficient-continual-learning-with-modular-1
Efficient Continual Learning with Modular Networks and Task-Driven Priors
2012.12631
https://arxiv.org/abs/2012.12631v2
https://arxiv.org/pdf/2012.12631v2.pdf
https://github.com/facebookresearch/CTrLBenchmark
true
true
true
pytorch
https://paperswithcode.com/paper/graph-and-temporal-convolutional-networks-for
Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos
2012.11806
https://arxiv.org/abs/2012.11806v3
https://arxiv.org/pdf/2012.11806v3.pdf
https://github.com/3dpose/GnTCN
true
true
true
pytorch
https://paperswithcode.com/paper/simulating-quantum-repeater-strategies-for
Simulating quantum repeater strategies for multiple satellites
2110.15806
https://arxiv.org/abs/2110.15806v2
https://arxiv.org/pdf/2110.15806v2.pdf
https://github.com/jwallnoefer/multisat_qrepeater_sim_archive
true
false
false
none
https://paperswithcode.com/paper/effective-deployment-of-cnns-for-3dof-pose
Effective Deployment of CNNs for 3DoF Pose Estimation and Grasping in Industrial Settings
2012.13210
https://arxiv.org/abs/2012.13210v1
https://arxiv.org/pdf/2012.13210v1.pdf
https://github.com/m4nh/loop
true
true
false
none
https://paperswithcode.com/paper/mutual-exclusivity-training-and-primitive
Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality
2211.15578
https://arxiv.org/abs/2211.15578v1
https://arxiv.org/pdf/2211.15578v1.pdf
https://github.com/owenzx/met-primaug
true
true
true
pytorch
https://paperswithcode.com/paper/hournas-extremely-fast-neural-architecture
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
2005.14446
https://arxiv.org/abs/2005.14446v3
https://arxiv.org/pdf/2005.14446v3.pdf
https://github.com/mindspore-ai/models/tree/master/research/cv/HourNAS
false
false
false
mindspore
https://paperswithcode.com/paper/a-flexible-approach-for-predictive-biomarker
A Flexible Approach for Predictive Biomarker Discovery
2205.01285
https://arxiv.org/abs/2205.01285v2
https://arxiv.org/pdf/2205.01285v2.pdf
https://github.com/philboileau/pub_unicate
true
true
false
none
https://paperswithcode.com/paper/arbitrary-style-transfer-in-real-time-with
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
1703.06868
http://arxiv.org/abs/1703.06868v2
http://arxiv.org/pdf/1703.06868v2.pdf
https://github.com/srihari-humbarwadi/adain-tensorflow2.x
false
false
true
tf
https://paperswithcode.com/paper/improving-lognormal-models-for-cosmological
Improving lognormal models for cosmological fields
1602.08503
http://arxiv.org/abs/1602.08503v3
http://arxiv.org/pdf/1602.08503v3.pdf
https://github.com/j-hw-wong/swept
false
false
true
none
https://paperswithcode.com/paper/cpp-taskflow-a-general-purpose-parallel-and
Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing System
2004.10908
https://arxiv.org/abs/2004.10908v4
https://arxiv.org/pdf/2004.10908v4.pdf
https://github.com/ojassm/stabletaskflow3
false
false
true
tf
https://paperswithcode.com/paper/oscillations-in-planar-deficiency-one-mass
Oscillations in planar deficiency-one mass-action systems
2103.00972
https://arxiv.org/abs/2103.00972v1
https://arxiv.org/pdf/2103.00972v1.pdf
https://github.com/balazsboros/reaction_networks/tree/main/dfc1thm_oscillation
true
false
false
none
https://paperswithcode.com/paper/probabilistically-robust-learning-balancing
Probabilistically Robust Learning: Balancing Average- and Worst-case Performance
2202.01136
https://arxiv.org/abs/2202.01136v3
https://arxiv.org/pdf/2202.01136v3.pdf
https://github.com/arobey1/advbench
true
true
false
pytorch
https://paperswithcode.com/paper/encoder-decoder-with-atrous-separable
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
1802.02611
http://arxiv.org/abs/1802.02611v3
http://arxiv.org/pdf/1802.02611v3.pdf
https://github.com/Vujas-Eteph/CiVOS
false
false
true
pytorch
https://paperswithcode.com/paper/a-distributed-black-box-adversarial-attack
A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm Optimization
null
https://www.mdpi.com/1424-8220/20/24/7158/htm
https://www.mdpi.com/1424-8220/20/24/7158/pdf
https://github.com/naufalso/distributed-blackbox-adv-attack
true
false
false
none
https://paperswithcode.com/paper/a-linear-approximation-method-for-the-shapley
A Linear Approximation Method for the Shapley Value
null
https://www.sciencedirect.com/science/article/pii/S0004370208000696
https://reader.elsevier.com/reader/sd/pii/S0004370208000696?token=191F2491A79C4261B0F079E188FC081A1E11BA3013811FE3B2059CB2F91E3E4272E074EC3B16BC7756B996ECA920C9EE
https://github.com/benedekrozemberczki/shapley
false
false
false
none
https://paperswithcode.com/paper/simulation-and-control-of-deformable
Simulation and Control of Deformable Autonomous Airships in Turbulent Wind
2012.15684
https://arxiv.org/abs/2012.15684v1
https://arxiv.org/pdf/2012.15684v1.pdf
https://github.com/robot-perception-group/airship_simulation
true
true
true
none
https://paperswithcode.com/paper/the-jsrealb-text-realizer-organization-and
The jsRealB Text Realizer: Organization and Use Cases -- Revised version
2012.15425
https://arxiv.org/abs/2012.15425v2
https://arxiv.org/pdf/2012.15425v2.pdf
https://github.com/rali-udem/JSrealB
true
true
false
none
https://paperswithcode.com/paper/image-processing-and-machine-learning-for
Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package
2308.09375
https://arxiv.org/abs/2308.09375v3
https://arxiv.org/pdf/2308.09375v3.pdf
https://github.com/behnoodrasti/hysupp
true
true
false
pytorch
https://paperswithcode.com/paper/the-ldbc-social-network-benchmark
The LDBC Social Network Benchmark
2001.02299
https://arxiv.org/abs/2001.02299v9
https://arxiv.org/pdf/2001.02299v9.pdf
https://github.com/ldbc/ldbc_snb_datagen
true
true
false
none
https://paperswithcode.com/paper/quick-annotator-an-open-source-digital
Quick Annotator: an open-source digital pathology based rapid image annotation tool
2101.02183
https://arxiv.org/abs/2101.02183v1
https://arxiv.org/pdf/2101.02183v1.pdf
https://github.com/choosehappy/QuickAnnotator
true
true
true
pytorch
https://paperswithcode.com/paper/what-do-deep-networks-like-to-see
What do Deep Networks Like to See?
1803.08337
http://arxiv.org/abs/1803.08337v1
http://arxiv.org/pdf/1803.08337v1.pdf
https://github.com/spalaciob/s2snets-reconstruction
true
false
false
pytorch
https://paperswithcode.com/paper/pyautofit-a-classy-probabilistic-programming
PyAutoFit: A Classy Probabilistic Programming Language for Model Composition and Fitting
2102.04472
https://arxiv.org/abs/2102.04472v1
https://arxiv.org/pdf/2102.04472v1.pdf
https://github.com/Jammy2211/autofit_workspace
true
true
false
none
https://paperswithcode.com/paper/continuous-control-with-deep-reinforcement
Continuous control with deep reinforcement learning
1509.02971
https://arxiv.org/abs/1509.02971v6
https://arxiv.org/pdf/1509.02971v6.pdf
https://github.com/iDataist/Continuous-Control-with-Deep-Deterministic-Policy-Gradient
false
false
true
pytorch
https://paperswithcode.com/paper/prioritized-experience-replay
Prioritized Experience Replay
1511.05952
http://arxiv.org/abs/1511.05952v4
http://arxiv.org/pdf/1511.05952v4.pdf
https://github.com/iDataist/Continuous-Control-with-Deep-Deterministic-Policy-Gradient
false
false
true
pytorch
https://paperswithcode.com/paper/unsupervised-learning-of-view-invariant
Unsupervised Learning of View-invariant Action Representations
1809.01844
http://arxiv.org/abs/1809.01844v1
http://arxiv.org/pdf/1809.01844v1.pdf
https://github.com/minostauros/VIAR
false
false
false
pytorch
https://paperswithcode.com/paper/sdgnn-learning-node-representation-for-signed
SDGNN: Learning Node Representation for Signed Directed Networks
2101.02390
https://arxiv.org/abs/2101.02390v4
https://arxiv.org/pdf/2101.02390v4.pdf
https://github.com/huangjunjie95/SiGAT
true
true
false
pytorch
https://paperswithcode.com/paper/arc-support-line-segments-revisited-an
Arc-support Line Segments Revisited: An Efficient and High-quality Ellipse Detection
1810.03243
https://arxiv.org/abs/1810.03243v5
https://arxiv.org/pdf/1810.03243v5.pdf
https://github.com/qxiaofan/awesome-High-quality-ellipse-detection
false
false
true
none
https://paperswithcode.com/paper/scalable-coverage-path-planning-of-multi
Scalable Coverage Path Planning of Multi-Robot Teams for Monitoring Non-Convex Areas
2103.14709
https://arxiv.org/abs/2103.14709v1
https://arxiv.org/pdf/2103.14709v1.pdf
https://github.com/adamslab-ub/SCoPP
true
true
false
none
https://paperswithcode.com/paper/yolov3-an-incremental-improvement
YOLOv3: An Incremental Improvement
1804.02767
http://arxiv.org/abs/1804.02767v1
http://arxiv.org/pdf/1804.02767v1.pdf
https://github.com/maiminh1996/YOLOv3-tensorflow
false
false
false
tf
https://paperswithcode.com/paper/instantaneous-psd-estimation-for-speech
Instantaneous PSD Estimation for Speech Enhancement based on Generalized Principal Components
2007.00542
https://arxiv.org/abs/2007.00542v1
https://arxiv.org/pdf/2007.00542v1.pdf
https://github.com/tdietzen/INST-PSD
true
false
false
none
https://paperswithcode.com/paper/wasserstein-divergence-for-gans
Wasserstein Divergence for GANs
1712.01026
http://arxiv.org/abs/1712.01026v4
http://arxiv.org/pdf/1712.01026v4.pdf
https://github.com/Lornatang/WassersteinGAN_DIV-PyTorch
false
false
true
pytorch
https://paperswithcode.com/paper/arttrack-articulated-multi-person-tracking-in
ArtTrack: Articulated Multi-person Tracking in the Wild
1612.01465
http://arxiv.org/abs/1612.01465v3
http://arxiv.org/pdf/1612.01465v3.pdf
https://github.com/ZJX9999/art_track
false
false
false
mindspore
https://paperswithcode.com/paper/meshsdf-differentiable-iso-surface-extraction
MeshSDF: Differentiable Iso-Surface Extraction
2006.03997
https://arxiv.org/abs/2006.03997v2
https://arxiv.org/pdf/2006.03997v2.pdf
https://github.com/cvlab-epfl/MeshSDF
true
false
true
pytorch
https://paperswithcode.com/paper/smart-car-features-using-embedded-systems-and
Smart Car Features using Embedded Systems and IoT
2101.00496
https://arxiv.org/abs/2101.00496v1
https://arxiv.org/pdf/2101.00496v1.pdf
https://github.com/Abhishek0697/IoT_SmartCar
true
true
true
none
https://paperswithcode.com/paper/polarizations-of-abelian-varieties-over
Polarizations of abelian varieties over finite fields via canonical liftings
2101.05531
https://arxiv.org/abs/2101.05531v3
https://arxiv.org/pdf/2101.05531v3.pdf
https://github.com/stmar89/PolsAbVarFpCanLift
true
true
false
none
https://paperswithcode.com/paper/recurrent-models-of-visual-attention
Recurrent Models of Visual Attention
1406.6247
http://arxiv.org/abs/1406.6247v1
http://arxiv.org/pdf/1406.6247v1.pdf
https://github.com/ulstu/ml
false
false
true
none
https://paperswithcode.com/paper/validating-bayesian-inference-algorithms-with
Validating Bayesian Inference Algorithms with Simulation-Based Calibration
1804.06788
http://arxiv.org/abs/1804.06788v1
http://arxiv.org/pdf/1804.06788v1.pdf
https://github.com/wlandau/targets-stan
false
false
true
none
https://paperswithcode.com/paper/predicting-the-next-best-view-for-3d-mesh
Predicting the Next Best View for 3D Mesh Refinement
1805.06207
http://arxiv.org/abs/1805.06207v1
http://arxiv.org/pdf/1805.06207v1.pdf
https://github.com/luca-morreale/stochastic-nbv-4-mesh-refinement
true
false
false
none
https://paperswithcode.com/paper/demo-linux-goes-apple-picking-cross-platform
Demo: Linux Goes Apple Picking: Cross-Platform Ad hoc Communication with Apple Wireless Direct Link
1812.06743
http://arxiv.org/abs/1812.06743v1
http://arxiv.org/pdf/1812.06743v1.pdf
https://github.com/seemoo-lab/owl
true
false
false
none
https://paperswithcode.com/paper/one-billion-apples-secret-sauce-recipe-for
One Billion Apples' Secret Sauce: Recipe for the Apple Wireless Direct Link Ad hoc Protocol
1808.03156
https://arxiv.org/abs/1808.03156v1
https://arxiv.org/pdf/1808.03156v1.pdf
https://github.com/seemoo-lab/owl
true
false
false
none
https://paperswithcode.com/paper/mat-fly-an-educational-platform-for
MAT-Fly: an educational platform for simulating Unmanned Aerial Vehicles aimed to detect and track moving objects
1904.00378
http://arxiv.org/abs/1904.00378v3
http://arxiv.org/pdf/1904.00378v3.pdf
https://github.com/gsilano/MAT-Fly
true
true
false
none
https://paperswithcode.com/paper/monte-carlo-graph-search-for-alphazero
Monte-Carlo Graph Search for AlphaZero
2012.11045
https://arxiv.org/abs/2012.11045v1
https://arxiv.org/pdf/2012.11045v1.pdf
https://github.com/QueensGambit/CrazyAra
true
true
true
mxnet
https://paperswithcode.com/paper/learning-to-play-the-chess-variant-crazyhouse
Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data
1908.06660
https://arxiv.org/abs/1908.06660v2
https://arxiv.org/pdf/1908.06660v2.pdf
https://github.com/QueensGambit/CrazyAra
true
true
true
mxnet
https://paperswithcode.com/paper/deepmapping-unsupervised-map-estimation-from
DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds
1811.11397
http://arxiv.org/abs/1811.11397v2
http://arxiv.org/pdf/1811.11397v2.pdf
https://github.com/ai4ce/DeepMapping
true
false
true
pytorch
https://paperswithcode.com/paper/pade-activation-units-end-to-end-learning-of
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks
1907.06732
https://arxiv.org/abs/1907.06732v3
https://arxiv.org/pdf/1907.06732v3.pdf
https://github.com/ml-research/rational_sl
false
false
true
pytorch
https://paperswithcode.com/paper/residual-networks-behave-like-ensembles-of
Residual Networks Behave Like Ensembles of Relatively Shallow Networks
1605.06431
http://arxiv.org/abs/1605.06431v2
http://arxiv.org/pdf/1605.06431v2.pdf
https://github.com/ml-research/rational_sl
false
false
true
pytorch
https://paperswithcode.com/paper/towards-deep-learning-models-resistant-to
Towards Deep Learning Models Resistant to Adversarial Attacks
1706.06083
https://arxiv.org/abs/1706.06083v4
https://arxiv.org/pdf/1706.06083v4.pdf
https://github.com/arobey1/advbench
false
false
true
pytorch
https://paperswithcode.com/paper/efficientnetv2-smaller-models-and-faster
EfficientNetV2: Smaller Models and Faster Training
2104.00298
https://arxiv.org/abs/2104.00298v3
https://arxiv.org/pdf/2104.00298v3.pdf
https://github.com/jahongir7174/EfficientNetV2
false
false
true
pytorch
https://paperswithcode.com/paper/mathcal-l-2-optimal-reduced-order-modeling
$\mathcal{L}_2$-optimal Reduced-order Modeling Using Parameter-separable Forms
2206.02929
https://arxiv.org/abs/2206.02929v2
https://arxiv.org/pdf/2206.02929v2.pdf
https://github.com/pmli/l2-opt-rom-ex
true
true
true
none
https://paperswithcode.com/paper/proof-repair-across-type-equivalences
Proof Repair across Type Equivalences
2010.00774
https://arxiv.org/abs/2010.00774v4
https://arxiv.org/pdf/2010.00774v4.pdf
https://github.com/uwplse/ornamental-search
true
true
true
none
https://paperswithcode.com/paper/studying-ad-library-integration-strategies-of
Studying Ad Library Integration Strategies of Top Free-to-Download Apps
2104.00182
https://arxiv.org/abs/2104.00182v1
https://arxiv.org/pdf/2104.00182v1.pdf
https://github.com/SAILResearch/suppmaterial-18-ahsan-ads_consumer_apps
true
false
false
none
https://paperswithcode.com/paper/unsupervised-neural-hidden-markov-models
Unsupervised Neural Hidden Markov Models
1609.09007
http://arxiv.org/abs/1609.09007v1
http://arxiv.org/pdf/1609.09007v1.pdf
https://github.com/ketranm/neuralHMM
true
true
true
torch
https://paperswithcode.com/paper/finding-novelty-with-uncertainty
Finding novelty with uncertainty
2002.04626
https://arxiv.org/abs/2002.04626v1
https://arxiv.org/pdf/2002.04626v1.pdf
https://github.com/jcreinhold/uncertaintorch
false
false
true
pytorch
https://paperswithcode.com/paper/high-dimensional-continuous-control-using
High-Dimensional Continuous Control Using Generalized Advantage Estimation
1506.02438
http://arxiv.org/abs/1506.02438v6
http://arxiv.org/pdf/1506.02438v6.pdf
https://github.com/DLR-RM/stable-baselines3
false
false
false
pytorch
https://paperswithcode.com/paper/coalitional-strategies-for-efficient
Coalitional strategies for efficient individual prediction explanation
2104.00765
https://arxiv.org/abs/2104.00765v1
https://arxiv.org/pdf/2104.00765v1.pdf
https://github.com/kaduceo/coalitional_explanation_methods
true
true
false
none
https://paperswithcode.com/paper/action-based-conversations-dataset-a-corpus
Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems
2104.00783
https://arxiv.org/abs/2104.00783v1
https://arxiv.org/pdf/2104.00783v1.pdf
https://github.com/asappresearch/abcd
true
true
true
pytorch
https://paperswithcode.com/paper/transferable-visual-words-exploiting-the
Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learning
2102.10680
https://arxiv.org/abs/2102.10680v1
https://arxiv.org/pdf/2102.10680v1.pdf
https://github.com/fhaghighi/TransVW
false
false
true
pytorch
https://paperswithcode.com/paper/a-geodesical-skew-divergence
$α$-Geodesical Skew Divergence
2103.17060
https://arxiv.org/abs/2103.17060v4
https://arxiv.org/pdf/2103.17060v4.pdf
https://github.com/nocotan/geodesical_skew_divergence
true
true
true
pytorch
https://paperswithcode.com/paper/counterfactual-data-augmentation-via
Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues
2210.16838
https://arxiv.org/abs/2210.16838v1
https://arxiv.org/pdf/2210.16838v1.pdf
https://github.com/ictnlp/capt
true
true
false
none
https://paperswithcode.com/paper/how-to-compute-invariant-manifolds-and-their
How to Compute Invariant Manifolds and their Reduced Dynamics in High-Dimensional Finite-Element Models
2103.10264
https://arxiv.org/abs/2103.10264v2
https://arxiv.org/pdf/2103.10264v2.pdf
https://github.com/haller-group/SSMTool-2.0
false
false
true
none
https://paperswithcode.com/paper/torchdeq-a-library-for-deep-equilibrium
TorchDEQ: A Library for Deep Equilibrium Models
2310.18605
https://arxiv.org/abs/2310.18605v1
https://arxiv.org/pdf/2310.18605v1.pdf
https://github.com/locuslab/torchdeq
true
true
false
pytorch
https://paperswithcode.com/paper/a-deep-reinforcement-learning-framework-for
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
1706.10059
http://arxiv.org/abs/1706.10059v2
http://arxiv.org/pdf/1706.10059v2.pdf
https://github.com/iffiX/PGPortfolio-pytorch
false
false
true
pytorch
https://paperswithcode.com/paper/parser-free-virtual-try-on-via-distilling
Parser-Free Virtual Try-on via Distilling Appearance Flows
2103.04559
https://arxiv.org/abs/2103.04559v2
https://arxiv.org/pdf/2103.04559v2.pdf
https://github.com/geyuying/PF-AFN
true
true
true
pytorch
https://paperswithcode.com/paper/dissimilarity-mixture-autoencoder-for-deep
Dissimilarity Mixture Autoencoder for Deep Clustering
2006.08177
https://arxiv.org/abs/2006.08177v4
https://arxiv.org/pdf/2006.08177v4.pdf
https://github.com/juselara1/dmae
true
true
true
tf